Data Sets Provide Different Insights to Different People
It’s interesting how a single set of data can have different meanings to different people. A Customer Care manager may glean insights from the data one way, a Product Manager another way, and a Business Analyst yet another way. We previously posted an article Data Analytics on Chat Sessions, where we analyzed the unstructured text data of website chat for a company in the Financial Services industry. This is a good use case for demonstrating how different business users can glean different insights from the same data source.
Our SignalsTM platform for text analytics is a self-service tool that anyone, from C-suite to front line employee, can use to glean insights from customer data. Let’s see how various internal stakeholders view this single data set of chat text differently.
Customer Care
The chat data in our previous study included the interaction between the website agents and the website visitors. The data included structured fields, such as agent ID, as well as the unstructured text data on the communication with the customer. Using the SignalsTM Custom Widget function, the customer care manager can glean insights from the chat data source to evaluate performance by agent. (See related post: Custom Filters Yield Customer Insights.)
The customer care manager now has a tool to go beyond basic information such as volume of transactions, and time spent. Managers can evaluate the effectiveness of the agents’ transactions with the website visitors. Is the agent chat using the approved company responses, or does an agent deviate from the training? This information is terrific for monitoring new agents, as well as managing experienced agents to ensure performance expectations are being met.
Product Manager
SignalsTM shows what people are saying, displaying the most common topics, issues, and trends. This insight is invaluable to the Product Manager for understanding product related issues as well as opportunities. One way a product manager can leverage SignalsTM is viewing the pain points being expressed by the website visitors. Analysis of the unstructured text of chat yielded insights on:
- Specific products
- Issues by product
- Trending of issues over time
It was not surprising to see the IRA product chat conversations peak during the spring tax season.
A second way the product manager can use the data is to compare what customers are saying versus what prospects are saying. Viewing these insights is as easy as changing the custom filter to pivot on this variable. Prospects often have pre-purchase questions that were not found in the online FAQ. Existing customers may have issues in using a product, or questions on a new product they want to add. Both viewpoints will aid the product manager in developing action plans to improve product information and communication.
A third way product managers can use this same data set is for comparing customers by category. For example, the data can be shown by demographic generation, enabling the product manager to see differences in the chat communication by age group. What are Millennials saying versus Baby Boomers? What are Gen X customers saying versus the Silent Generation? Alternatively, the chat data can be filtered to show customers by their account balance, their volume of transactions, geographic location, or another data point. The product manager can use the SignalsTM platform on a regular basis to stay on top of product conversations in near real-time.
Business Analyst
Usability is one of the most important aspects for the analyst charged with collecting customer experience information for an organization. For instance, a data analyst can examine customer feedback on the chat sessions. Analysts also can analyze other sources of data, including unstructured text data included in web surveys, email surveys and computer direct interviews, for monitoring customer communications on social media and other Internet forums.
Solving Problems is another important aspect of the analyst job. A data analyst solves problems that directly involve an organization’s customers. For example, if a chat user informs the company that an employee wrongly posted information demeaning the customer on social media, the customer experience data analyst moves in quickly to manage the crisis. She can advise management to issue an apology, explain the incident and outline the steps the firm is taking against employees responsible for posting such information. Data analysts also help to develop public relations and communication strategies that an organization uses to manage chat conversations with its customers.
These are just three examples of how different employees in an organization can get value from a common data set. SignalsTM brings different data silos together into one common visualization platform that end-users can access on a daily basis. We make self-service analytics consumable by everyone, enabling users to make more data-driven decisions.
Try a self-service data analytics account for yourself. Contact us at clientsupport@stratifyd.com and we’ll be glad to help!
